Advertisement

Discovering Spatio-Temporal Latent Influence in Geographical Attention Dynamics

  • Minoru Higuchi
  • Kanji Matsutani
  • Masahito Kumano
  • Masahiro KimuraEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11052)

Abstract

We address the problem of modeling the occurrence process of events for visiting attractive places, called points-of-interest (POIs), in a sightseeing city in the setting of a continuous time-axis and a continuous spatial domain, which is referred to as modeling geographical attention dynamics. By combining a Hawkes process with a time-varying Gaussian mixture model in a novel way and incorporating the influence structure depending on time slots as well, we propose a probabilistic model for discovering the spatio-temporal influence structure among major sightseeing areas from the viewpoint of geographical attention dynamics, and aim to accurately predict POI visit events in the near future. We develop an efficient method of inferring the parameters in the proposed model from the observed sequence of POI visit events, and present an analysis method for the geographical attention dynamics. Using real data of POI visit events in a Japanese sightseeing city, we demonstrate that the proposed model outperforms conventional models in terms of predictive accuracy, and uncover the spatio-temporal influence structure among major sightseeing areas in the city from the perspective of geographical attention dynamics.

Keywords

Geographical attention dynamics Point process model Spatio-temporal influence structure 

Notes

Acknowledgments

This work was supported in part by JSPS KAKENHI Grant Number JP17K00433.

References

  1. 1.
    Aalen, O., Borgan, O., Gjessing, H.: Survival and Event History Analysis: A Process Point of View. Springer, New York (2008)CrossRefGoogle Scholar
  2. 2.
    Chen, C., Yang, H., Lyu, M., King, I.: Where you like to go next: successive point-of-interest recommendation. In: Proceedings of IJCAI 2013, pp. 2605–2611 (2013)Google Scholar
  3. 3.
    Chen, S., et al.: Interactive visual discovering of movement patterns from sparsely sampled geo-tagged social media data. IEEE Trans. Vis. Comput. Graph. 22(1), 270–279 (2016)CrossRefGoogle Scholar
  4. 4.
    Daneshmand, H., Gomez-Rodriguez, M., Song, L., Schölkopf, B.: Estimating diffusion network structures: recovery conditions, sample complexity and soft-thresholding algorithm. In: Proceedings of ICML 2014, pp. 793–801 (2014)Google Scholar
  5. 5.
    Du, N., Dai, H., Upadhyay, U., Gomez-Rodriguez, M., Song, L.: Recurrent marked temporal point processes: embedding event history to vector. In: Proceedings of KDD 2016, pp. 1555–1564 (2016)Google Scholar
  6. 6.
    Farajtabar, M., Du, N., Gomez-Rodriguez, M., Valera, I., Zha, H., Song, L.: Shaping social activity by incentivizing users. In: Proceedings of NIPS 2014, pp. 2474–2482 (2014)Google Scholar
  7. 7.
    Farajtabar, M., Wang, Y., Gomez-Rodriguez, M., Li, S., Zha, H., Song, L.: COEVOLVE: a joint point process model for information diffusion and network evolution. J. Mach. Learn. Res. 18(41), 1–49 (2017)MathSciNetzbMATHGoogle Scholar
  8. 8.
    Feng, S., Li, X., Zeng, Y., Cong, G., Chee, Y., Yuan, Q.: Personalized ranking metric embedding for next new poi recommendation. In: Proceedings of IJCAI 2015, pp. 2069–2075 (2015)Google Scholar
  9. 9.
    Frey, B., Dueck, D.: Clustering by passing messages between data points. Science 315(5814), 972–976 (2007)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Gao, H., Tang, J., Hu, X., Liu, H.: Exploring temporal effects for location recommendation on location-based social networks. In: Proceedings of RecSys 2013, pp. 93–100 (2013)Google Scholar
  11. 11.
    Gao, S., Ma, J., Chen, Z.: Modeling and predicting retweeting dynamics on microblogging platforms. In: Proceedings of WSDM 2015, pp. 107–116 (2015)Google Scholar
  12. 12.
    Gomez-Rodriguez, M., Leskovec, J., Krause, A.: Inferring networks of diffusion and influence. In: Proceedings of KDD 2010, pp. 1019–1028 (2010)Google Scholar
  13. 13.
    Hawkes, A.: Spectra of some self-exciting and mutually exiting point process. Biometrika 58(1), 83–90 (1971)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Liu, Y., Liu, C., Liu, B., Qu, M., Xiong, H.: Unified point-of-interest recommendation with temporal interval assessment. In: Proceedings of KDD 2016, pp. 1015–1024 (2016)Google Scholar
  15. 15.
    Ogata, Y.: On lewis’ simulation method for point processes. IEEE Trans. Inf. Theory 27(1), 23–31 (1981)CrossRefGoogle Scholar
  16. 16.
    Shen, H., Wang, D., Song, C., Barabási, A.L.: Modeling and predicting popularity dynamics via reinforced poisson processes. In: Proceedings of AAAI 2014, pp. 291–297 (2014)Google Scholar
  17. 17.
    Shin, S., et al.: STExNMF: spatio-temporally exclusive topic discovery for anomalous event detection. In: Proceedings of ICDM 2017, pp. 435–444 (2017)Google Scholar
  18. 18.
    Stephens, M.: Bayesian analysis of mixture models with an unknown number of components: an alternative to reversible jump methods. Ann. Stat. 28(1), 40–74 (2000)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Wang, D., Song, C., Barabási, A.L.: Quantifying long-term scientific impact. Science 342(6154), 127–132 (2013)CrossRefGoogle Scholar
  20. 20.
    Yuan, Q., Cong, G., Ma, Z., Sun, A., Magnenat-Thalmann, N.: Time-aware point-of-interest recommendation. In: Proceedings of SIGIR 2013, pp. 363–372 (2013)Google Scholar
  21. 21.
    Zhang, J., Chow, C.: Spatiotemporal sequential influence modeling for location recommendations: a gravity-based approach. ACM Trans. Intell. Syst. Technol. 7(1), 11:1–11:25 (2015)CrossRefGoogle Scholar
  22. 22.
    Zhao, Q., Erdogdu, M., He, H., Rajaraman, A., Leskovec, J.: SEISMIC: a self-exciting point process model for predicting tweet popularity. In: Proceedings of KDD 2015, pp. 1513–1522 (2015)Google Scholar
  23. 23.
    Zhou, K., Zha, H., Song, L.: Learning social infectivity in sparse low-rank networks using multi-dimensional hawkes processes. In: Proceedings of AISTATS 2013, pp. 641–649 (2013)Google Scholar
  24. 24.
    Zhou, Z., Matteson, D., Woodard, D., Henderson, S., Micheas, A.: A spatio-temporal point process model for ambulance demand. J. Am. Stat. Assoc. 110(509), 6–15 (2015)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Minoru Higuchi
    • 1
  • Kanji Matsutani
    • 2
  • Masahito Kumano
    • 1
  • Masahiro Kimura
    • 1
    Email author
  1. 1.Department of Electronics and InformaticsRyukoku UniversityOtsuJapan
  2. 2.Tokai Regional Headquarters, NTT West CorporationNagoyaJapan

Personalised recommendations